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S ystemsAnalysis LaboratoryHelsinki University of Technology
Decision and Negotiation Support in Multi-Stakeholder Development of Lake
Regulation Policy
Raimo P. Hämäläinen1, Eero Kettunen1,Mika Marttunen2, and Harri Ehtamo1
1 Systems Analysis Laboratory, Helsinki University of Technology2 Finnish Environment Institute
http://www.hut.fi/Units/Systems.Analysis/
Report on the testing phase-
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S ystemsAnalysis LaboratoryHelsinki University of Technology
The Framework
1. Structuring the problem
2. Identifying Pareto-optimal alternatives
3. Seeking group consensus
4. Seeking public acceptance
• Objective to provide support for the whole decision process
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Information Technology
Problem Structuring - comparison of policy alternatives:HIPRE 3+Web-HIPRE
Dynamic policy alternatives:
ISMO - Interactive analysis of dynamic waterregulation Strategies by Multicriteria Optimization
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Group-consensus:HIPRE Grouplink (Interval AHP model)WINPRE - Workbench for Interval PreferenceProgramming (Interval AHP, SMART/SWING)
Opinion Online - Web-based survey and votingPublic - acceptance:
Pareto-optimal policies:Joint Gains - Generating efficientalternatives (in testing with simplefied goals)
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S ystemsAnalysis LaboratoryHelsinki University of Technology
• Illustrative reference case
• Regulation policy defined by annual water level goals
• Stakeholders with conflicting objectives– Hydro power producers, fishermen, farmers, ...
• First phase of true testing– Role playing experiments
Development of Water Level Management Policy in Lake Päijänne
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S ystemsAnalysis LaboratoryHelsinki University of Technology
LakePyhäjärvi
Lake Päijänne
Inflow
xp(t)A p(t)
9 1011
2
8
7
6
4
3
12
q(t) = Control
qL1 (t)
LakesRuotsalainen and Konnivesi
1
qp(t)
q2(t)
q21 (t)
5
Gulf of Finland
q in(t)
x(t)
A(t)
q23 (t)q22 (t)
q212 (t)q211 (t)
x(t)
A(t)
Inflow
dam
lakewater flowpower plant
qL2 (t)Inflow
LAKEPÄIJÄNNE
LAKES RUOTSALAINEN AND KONNIVESI
RIVERKYMIJOKI
0 10 20 30 40 50km
LAKEPYHÄJÄRVI
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Need for modeling and decision support
• Dynamic system
• No intuitive solutions - impacts are functions of decision variables
• Interactive analysis of impacts
• Multiple criteria
• Many stakeholder groups
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Average Water Levels of Lake Päijänne
77.80
77.90
78.00
78.10
78.20
78.30
78.40
78.50
78.60
1 2 3 4 5 6 7 8 9 10 11 12
Month
NN
+mRegulated Unregulated
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S ystemsAnalysis LaboratoryHelsinki University of Technology
76.50
77.00
77.50
78.00
78.50
79.00
79.50
80.00
1.1 25.5 16.10 9.3 31.7 22.12 15.5 6.10 27.2 20.7 11.12
Goal levelMin levelMax levelwater level
Water level
76.50
77.00
77.50
78.00
78.50
79.00
79.50
80.00
1.1 25.5 16.10 9.3 31.7 22.12 15.5 6.10 27.2 20.7 11.12
Goal levelMin levelMax levelwater level
Water level
0
100
200
300
400
500
600
700
30.12 23.5 14.10 7.3 29.7 20.12 13.5 4.10 25.2 18.7 9.12
Min flow
Max flow
Outflow
Outflow
0
100
200
300
400
500
600
700
30.12 23.5 14.10 7.3 29.7 20.12 13.5 4.10 25.2 18.7 9.12
Min flow
Max flow
Outflow
Outflow
Utopia solution Realistic solution
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Structuring the Problem
• Iterative value tree analysis– Hierarchical structuring and prioritization
– Decision criteria
– Learning the ranges by initial prioritizations with temporary alternatives
– Stakeholder grouping
• Decision variables defining regulation policy– Target water levels at April 1st and September 1st
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Method of Improving Directions
• Ehtamo, Kettunen and Hämäläinen (1998)
• Interactive method for identification of efficient alternatives - Joint Gains software
• Subjects are onlygiven simple comparison tasks:
“Which one of these alternatives do you prefer most?” or
“Which one of these two alternatives do you prefer, A or B?”
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Pareto-efficiency in group settings
Inefficient alternative:
Alternatives preferred to x
by DM1 Alternatives preferred to x
by DM2
x
Efficient alternative:
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S ystemsAnalysis LaboratoryHelsinki University of Technology
x1
x2
Most preferred alternative on the circle
Approximation at x
x
Approximating DM’s utility function’s gradient direction
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S ystemsAnalysis LaboratoryHelsinki University of Technology
• Required preference information: DMs’ utility functions’ gradient directions
• Solution of a nonlinear direction finding optimization problem– Special case with two DMs: bisecting direction
Calculation of jointly improving direction
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S ystemsAnalysis LaboratoryHelsinki University of Technology
x1
x2
DM1
Jointly improvingdirectionx
DM2
DM1
DM2
Iteration step
• DMs select most preferred points in this direction
• New iteration point: nearest
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Generation of efficient frontier from different initial points
Efficient frontier
x2
x1
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Joint Gains -Negotiation Support System
Joint Gains Mediator
Joint GainsDM interface
Subject 5:“power company”
Joint GainsDM interface
Subject 4:“farmer”
Joint GainsDM interface
Subject 3:“fisherman”
Joint GainsDM interface
Subject 2:“summer resident”
Joint GainsDM interface
Subject 1:“environmentalist”
Local area network
questions
replies
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Interfaces for comparison tasks
Expected flood damage costs
0
1
2
3
A B
Mill
ion
s o
f F
IM
Width of potential vegetation zone
0
10
20
30
A B
cm
. . .
I prefer A to B.
I prefer B to A.
Which alternative do you prefer?
Expected flood damage costs
0
1
2
3
Mill
ion
s o
f F
IM
Width of potential vegetation zone
0
10
20
30
cm . . .
Choose the best alternative by scrolling the bar.
Scanning alternatives
Answer a series of pairwisecomparison questions
A
B
A B
etc.
or
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Proposal for jointly preferred alternative
X
Y
Expected flood damage costs
0
1
2
3
X Y
Mill
ion
s o
f F
IM
Width of potential vegetation zone
0
10
20
30
X Y
cm
. . .
Do you prefer alternative Y to X?
Yes No
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Role Playing Experiments• Roles (fisherman, environmentalist, summer
resident, farmer, power company) and objectives (e.g., high and diverse catch, natural reproduction) given
• 2 or 3 subjects in 9 test groups
Questions of interest:
• Subjects’ opinion about the tasks
• Consistency of statements
• Convergence speed
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Mediation processes for 2 DM groups
Fisherman &Environmentalist
Fisherman &Summer resident
Environmentalist &Farmer
initial and intermediate pointsstopping point
Roles:
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Mediation processes for 2 DM groups
Fisherman &Environmentalist
Fisherman &Power company
Power company &Environmentalist
initial and intermediate pointsstopping point
Roles:
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Mediation processes for 2 and 3 DM groups
Fisherman &Farmer
Farmer, Power company& Summer resident
Summer resident &Environmentalist
Roles:
initial and intermediate pointsstopping point
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Role playing experiments - observations
• Subjects found the stated questions easy to reply with both elicitation methods
• Statements and results were consistent with the given role objectives
• Experiment suggests a high speed of convergence
• Low degree of conflict (similar objectives) same nearby points reached from different initial points
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Seeking Group Consensus
• Select and evaluate a representative set of efficient alternatives by interval value tree analysis
• Objective to reach consensus
• Tools for consensus seeking– HIPRE 3+ Group Link
– WINPRE - Workbench for Interactive Preference Programming
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Individual AHP prioritizations (HIPRE)
Combination of prioritizations (Group Link)
Interval preference model (WINPRE)
RecreationRecreationLandscape
LandscapeBiodiversityBiodiversity
DM1
DM1
DM1
DM2
DM2
DM2
DM3
DM3
DM3
View from interval preference model for three DMs:
HIPRE Group Link
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S ystemsAnalysis LaboratoryHelsinki University of Technology
WINPRE - Workbench for Interactive Preference Programming (AHP mode)
Group priorities embedded in the interval statements
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Conclusion
• Framework for supporting complex decision processes– An evolutionary learning process
• Shown to be feasible by role playing experiments– Real application
– Testing of methods and tools
– Biases related to elicitation procedure tested
• Important testing phase often neglected– Allows improvements before final process
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S ystemsAnalysis LaboratoryHelsinki University of Technology
WWW-sites
Systems Analysis Laboratory Activity Report: http://www.hut.fi/Units/SAL/Research/.
WINPRE - Workbench for Interactive Preference Programming v. 1.0, Computer software, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Downloadables/.
Web-HIPRE - Java-applet for Value Tree and AHP Analysis, Computer software, Systems Analysis Laboratory, Helsinki University of Technology (http://www.hipre.hut.fi).
The Päijänne regulation policy project: (http://leino.hut.fi/päijänne.htm)
References
Ehtamo, H., R. P. Hämäläinen, P. Heiskanen, J. Teich, M. Verkama, and S. Zionts (1998), “Generating Pareto Solutions in Two-Party Negotiations by Adjusting Artificial Constraints,” Manuscript, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Publications/.
Ehtamo, H., E. Kettunen, and R. P. Hämäläinen (1998), “Searching for Joint Gains in Multi-Party Negotiations,” Manuscript, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Publications/.
R.P. Hämäläinen, E. Kettunen, M. Marttunen and H. Ehtamo: An approach to decision and negotiation support in multi-stakeholder development of lake regulation policy. Manuscript, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Publications/.
References
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Ehtamo, H., M. Verkama, and R. P. Hämäläinen (1992), “On Contracting under Incomplete Information Using Linear Proposals,” Preprints of the Fifth International Symposium on Dynamic Games and Applications, Grimentz, Switzerland, 128-133.
Ehtamo, H., M. Verkama, and R. P. Hämäläinen (1998), “How to Select Fair Improving Directions in a Negotiation Model over Continuous Issues,” IEEE Transactions on Systems, Man, and Cybernetics, to appear. A shortened version in Proceedings of the Decision Science Institute 1995 Annual Meeting, November 20-22, 1995, Boston, Massachusetts, 2, 549-551.
Hämäläinen, R. P. (1988), “Computer Assisted Energy Policy Analysis in the Parliament of Finland,” Interfaces, 18(4), 12-23.
Hämäläinen, R. P., A. A. Salo, and K. Pöysti (1991), “Observations about Consensus Seeking in a Multiple Criteria Environment,” Proceedings of the 25th Annual Hawaii International Conference on System Sciences, IEEE Computer Society Press, 4, 190-198.
Hämäläinen, R. P. and E. Kettunen (1994), “On-Line Group Decision Support by HIPRE 3+ Group Link,” Proceedings of the Third International Conference on the Analytic Hierarchy Process, July 11-13, 1994, George Washington University, Washington D.C., 547-557.
Hämäläinen, R. P. and H. Lauri (1998), HIPRE 3+ Decision Support Software v. 3.15b, Computer software, Systems Analysis Laboratory, Helsinki University of Technology.
Hämäläinen, R. P., and O. Leikola (1995), “Spontaneous Decision Conferencing in Parliamentary Negotiations,” Proceedings of the 28th Annual Hawaii International Conference on System Sciences, IEEE Computer Society Press, 4, 290-299.
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Hämäläinen, R. P., K. Sinkko, M. Lindstedt, M. Ammann, and A. Salo (1998), RODOS and Decision Conferencing on Early Stage Protective Actions in Finland, RODOS Report (WG7) EU Research Project on Decision Support for Nuclear Emergencies.
Hämäläinen, R. P. and J. Mäntysaari (1998), “Interactive Spreadsheet Modelling of Regulation Strategies for a Lake-River System,” Proceedings of the 17th IASTED International Conference on Modelling, Identification and Control, February 18-20, 1998, IASTED - Acta Press, Anaheim, Grindelwald, Switzerland, 181-184.
Hämäläinen, R. P. and M. Pöyhönen (1996), “On-Line Group Decision Support by Preference Programming in Traffic Planning,” Group Decision and Negotiation, 5, 485-500.
Marttunen, M. and R. P. Hämäläinen (1995), “Decision Analysis Interviews in Environmental Impact Assessment,” European Journal of Operational Research, 87, 551-563.
Pöyhönen, M., H. C. Vrolijk, and R. P. Hämäläinen (1997), Behavioral and Procedural Consequences of Structural Variation in Value Trees, Research Report A69, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Publications/.
Salo, A. A. and R. P. Hämäläinen (1992), “Preference Assessment by Imprecise Ratio Statements,” Operations Research, 40, 1053-1061.
Salo, A. A. and R. P. Hämäläinen (1995), “Preference Programming through Approximative Ratio Comparisons,” European Journal of Operational Research, 82, 458-475.
Salo A. (1995), “Interactive decision aiding for group decision support,” European Journal of Operational Research, 84, 134-149.
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